Description and Requirements
Role Summary
We are looking for an AI/ML Engineer with strong Python skills and a passion for building production-ready AI systems. In this role, you will contribute to developing and deploying LLM-based applications, including multi-agent systems and RAG pipelines, while ensuring scalability, reliability, and cost efficiency.
Key Responsibilities
- Model Integration:Assist in deploying and maintaining machine learning models into production environments using Python and cloud services.
- Agentic Frameworks:Hands-on experience with LangGraph, CrewAI, or AutoGento build multi-agent systems that can reason, plan, and execute multi-step tasks.
- RAG:Understanding of advanced Retrieval-Augmented Generation (RAG)techniques, including hybrid search, reranking, and semantic chunking using vector databases like Pinecone, Weaviate, or Chroma.
- Model Context Protocol (MCP):Familiarity with implementing MCP serversto help LLMs interact securely with external tools and data sources.
- Data Pipeline Support:Help clean, preprocess, and augment datasets to improve model training and evaluation.
- Prompt Engineering:Design, test, and iterate on system prompts to ensure high-quality and safe AI outputs.
- Token Optimization:Ability to manage context window limits and optimize token usage to balance model performance with operational costs.
- MLOps Basics:Experience withCI/CD pipelinestailored for AI, including model versioning and automated testing of prompts within the development lifecycle.
- Collaboration:Participate in code reviews, sprint planning, and technical documentation to ensure code quality and knowledge sharing.
- Continuous Learning:Stay up-to-date with the latest AI research, libraries, and tools to suggest improvements to our tech stack.
Qualifications:
- Education:Bachelor's degree in Computer Science, Data Science, or a related technical field (or equivalent practical experience).
- Programming:Proficiency inPython
- AI Foundations:Basic understanding of machine learning concepts (Supervised vs. Unsupervised learning)
- Web Basics:Familiarity with RESTful APIs and how to integrate them into a backend.
- Version Control:Solid grasp of Git and collaborative workflows.
- Problem Solving:A hacker mindset-willing to experiment, fail fast, and iterate on complex problems.
- Experience : 5+ years of relevant experience
